Article
Computer Science, Artificial Intelligence
Shahed Mahmud, Ripon K. Chakrabortty, Alireza Abbasi, Michael J. Ryan
Summary: The study introduces an integrated supply chain scheduling model to address highly customized and on-time delivery requirements, enhancing the performance of multi-objective particle swarm optimization. Two new meta-heuristic algorithms are developed with specific search mechanisms and mutation operators for the flexible job shop problem.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Rui Li, Wenyin Gong, Chao Lu
Summary: This paper presents a multiobjective method for solving the flexible job shop scheduling problem with fuzzy processing time. The proposed algorithm outperforms five state-of-the-art methods in three benchmark tests.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Leilei Meng, Chaoyong Zhang, Biao Zhang, Kaizhou Gao, Yaping Ren, Hongyan Sang
Summary: This paper addresses the flexible job shop scheduling problem with controllable processing times (FJSP-CPT) and proposes a mixed integer linear programming (MILP) model for small-scale instances. For medium and large-sized problems, an efficient multi-objective hybrid shuffled frog-leaping algorithm (MO-HS-FLA) is proposed. The algorithm includes an energy-efficient decoding method and a multi-objective variable local search (MO-VNS) algorithm.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Engineering, Industrial
Lu Zhang, Yi Feng, Qinge Xiao, Yunlang Xu, Di Li, Dongsheng Yang, Zhile Yang
Summary: This paper investigates the difficulties of Dynamic Flexible Job Shop Scheduling (DFJSP) caused by the uncertainties and complexity in the production process due to customized requirements. A new DFJSP model, VPT-FJSP, is proposed and solved using Markov decision process (MDP) and reinforcement learning methods. The experimental results show that the proposed framework outperforms genetic algorithm and ant colony optimization in most cases, demonstrating its effectiveness and robustness.
JOURNAL OF MANUFACTURING SYSTEMS
(2023)
Article
Engineering, Industrial
Wenkang Zhang, Yufan Zheng, Rafiq Ahmad
Summary: This work focuses on a multi-objective scheduling problem in a remanufacturing system that aims to reduce completion time and energy usage by determining the allocation/sequence of disassembly/reassembly jobs and the operation sequencing and workstation assignment of reprocessing jobs. A multi-objective mixed-integer programming model is developed, and an improved grey wolf optimization algorithm is introduced to achieve efficient scheduling. Experimental results demonstrate that the developed algorithm outperforms other existing methods in terms of solution accuracy, computing speed, solution stability, and convergence performance. Furthermore, a case study shows the algorithm's superiority in solving real-world remanufacturing scheduling problems in terms of energy usage and time cost reduction.
JOURNAL OF MANUFACTURING SYSTEMS
(2023)
Article
Automation & Control Systems
Zixiao Pan, Deming Lei, Ling Wang
Summary: This study focuses on energy-efficient fuzzy FJSP and proposes a bi-population evolutionary algorithm to optimize scheduling results. By handling uncertainty, dynamically adjusting population size, and using enhanced local search, the new method shows promising results in experiments.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Management
Karim Tamssaouet, Stephane Dauzere-Peres
Summary: This article presents a framework that unifies and generalizes well-known literature results on local search for job-shop and flexible job-shop scheduling problems. The proposed framework focuses on quickly ruling out infeasible moves and evaluating the quality of feasible neighbors, which are crucial for the success of local search approaches. It can be applied to any scheduling problem with an appropriate defined neighborhood structure. The proposed framework introduces novel procedures for evaluating feasibility and estimating the value of objective functions for neighbor solutions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Shu Luo, Linxuan Zhang, Yushun Fan
Summary: This paper proposes an on-line rescheduling framework named as two-hierarchy deep Q network (THDQN) for the dynamic multi-objective flexible job shop scheduling problem with new job insertions. By optimizing two practical objectives including total weighted tardiness and average machine utilization rate, the trained THDQN has shown effectiveness and generality on a wide range of test instances.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Guohui Zhang, Xixi Lu, Xing Liu, Litao Zhang, Shiwen Wei, Wenqiang Zhang
Summary: This research proposes an effective two-stage algorithm based on convolutional neural network for solving the flexible job shop scheduling problem. The algorithm is used to train the prediction model and evaluate the robustness of scheduling, with the evaluation done through the proposed RMn metric.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Mohsen Ziaee, Javad Mortazavi, Mohsen Amra
Summary: This paper studied a new extension of the flexible job shop scheduling problem using a mixed-integer linear programming model. Through a heuristic algorithm, an efficient solution was obtained for the problem.
Article
Computer Science, Artificial Intelligence
Guiliang Gong, Qianwang Deng, Xuran Gong, Dan Huang
Summary: This study highlights the importance of considering worker flexibility and green production related factors in a multi-objective flexible job-shop scheduling problem. A new non-dominated ensemble fitness ranking algorithm (NEFRL) is proposed to address this issue, and its effectiveness is demonstrated through comparisons with other multi-objective algorithms in 31 instances.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Yu Du, Junqing Li, Chengdong Li, Peiyong Duan
Summary: In this study, a DQN model is proposed to solve a multiobjective FJSP with crane transportation and setup times. The model optimizes makespan and total energy consumption simultaneously based on weighting approach. The DQN model uses 12 state features and seven actions to describe the scheduling process, and applies a novel structure in the DQN topology. Extensive computational tests and comparisons demonstrate the effectiveness and advantages of the proposed method in solving FJSP-CS.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Automation & Control Systems
Minghao Qu, Ying Zuo, Feng Xiang, Fei Tao
Summary: This paper proposes a many-objective optimization model to improve sustainability in the shop floor by considering makespan, total energy consumption, and other indicators. An improved algorithm is used to find the optimal or near-optimal solutions, and a real-life case study is conducted to validate the effectiveness of the model and algorithm.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2022)
Article
Computer Science, Hardware & Architecture
Fei Luan, Hongxuan Zhao, Shi Qiang Liu, Yixin He, Biao Tang
Summary: To achieve green targets, manufacturing enterprises need to propose an effective energy-saving strategy for production scheduling. In this paper, a multi-objective energy-saving flexible job shop-scheduling problem (MO_EFJSP) is formulated and solved using an enhanced non-dominated sorting genetic algorithm II (ENSGA-II). Extensive computational experiments prove the applicability of ENSGA-II in saving power consumption and its contribution to the field of green production scheduling.
SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS
(2023)
Article
Operations Research & Management Science
Yiyi Xu, M'hammed Sahnoun, Fouad Ben Abdelaziz, David Baudry
Summary: This paper proposes a new dynamic algorithm based on simulation approach and multi-objective optimization to solve the FJSP with transportation assignment. The results obtained from the computational experiments have shown that the proposed approach is efficient and competitive.
ANNALS OF OPERATIONS RESEARCH
(2022)
